no code implementations • 25 Dec 2023 • Sepehr Nourmohammadi, Shervin Rahimzadeh Arashloo
The vector-norm constraint enables the model to adapt to the intrinsic uniformity/sparsity of the ensemble in the space of base learners and acts as a (soft) classifier selection mechanism by shaping the relative magnitudes of fusion weights.
no code implementations • 22 Apr 2022 • Shervin Rahimzadeh Arashloo
The paper studies face spoofing, a. k. a.
no code implementations • 19 Aug 2020 • Shervin Rahimzadeh Arashloo
The paper addresses the multiple kernel learning (MKL) problem for one-class classification (OCC).
no code implementations • 31 Dec 2019 • Shervin Rahimzadeh Arashloo
The paper addresses face presentation attack detection in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that were not present in the training step.
no code implementations • 22 May 2019 • Shervin Rahimzadeh Arashloo, Josef Kittler
The non-linear structure learning method is then extended to a sparse setting where different tasks compete in an output composition mechanism, leading to a sparse non-linear structure among multiple problems.
no code implementations • 6 Feb 2019 • Shervin Rahimzadeh Arashloo, Josef Kittler
This work addresses these shortcomings by studying the effect of regularising the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation (OC-KSR).
no code implementations • 3 Jul 2018 • Shervin Rahimzadeh Arashloo, Josef Kittler
The paper introduces a new efficient nonlinear one-class classifier formulated as the Rayleigh quotient criterion optimisation.
no code implementations • 2 Jul 2018 • Shervin Rahimzadeh Arashloo, Josef Kittler
In addition, it is demonstrated that the same set of deep convolutional features used for the recognition purposes is effective for face presentation attack detection in the class-specific one-class anomaly detection paradigm.